Updated: 2020-09-03 07:48:34 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
West Virginia 1.42 10618 191
Rhode Island 1.28 20133 114
Delaware 1.20 17305 81
Tennessee 1.20 154936 1726
Florida 1.16 632230 3880
Pennsylvania 1.16 140435 780
Nebraska 1.15 34801 326
South Dakota 1.15 13884 303
Vermont 1.14 1628 9
Kentucky 1.13 52579 780
New Jersey 1.11 194065 340
Ohio 1.11 125743 1189
Missouri 1.10 78660 1228
New York 1.10 440928 711
Oklahoma 1.10 60167 786
Kansas 1.09 44311 662
Idaho 1.07 32986 313
Connecticut 1.06 53014 154
Louisiana 1.06 149556 744
Montana 1.03 7644 126
South Carolina 1.02 120706 886
Virginia 1.02 96500 725
Utah 1.01 52918 377
Maine 1.00 4578 24
Maryland 1.00 109846 532
Illinois 0.99 239625 1882
North Carolina 0.99 171358 1592
Minnesota 0.98 77052 744
Wisconsin 0.96 77859 657
North Dakota 0.95 12239 225
Texas 0.95 650380 4494
Alabama 0.94 128616 1196
California 0.94 721906 4879
Washington 0.94 78565 457
Wyoming 0.93 3897 31
New Hampshire 0.92 7310 18
Oregon 0.90 27125 204
Indiana 0.89 97949 867
Colorado 0.88 58341 272
Mississippi 0.88 84300 593
Georgia 0.87 256019 1882
New Mexico 0.87 25591 112
Arizona 0.86 202866 425
Michigan 0.85 114208 665
Arkansas 0.84 61228 463
Nevada 0.81 70040 376
Iowa 0.80 66801 766
Massachusetts 0.76 127432 244

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Whitman WA 19 1 1.5 623 1280 57
Spokane WA 5 2 1.2 5453 1100 50
Clark WA 8 3 1.3 2653 570 29
Whatcom WA 12 4 1.4 1124 520 8
Snohomish WA 4 5 1.0 7145 910 35
Pierce WA 3 6 1.0 7595 880 43
King WA 1 7 0.8 19851 920 91
Grant WA 9 8 1.0 2417 2550 29
Franklin WA 7 9 1.1 4113 4540 16
Yakima WA 2 10 0.9 11717 4700 21
Benton WA 6 15 0.7 4358 2240 14
OR
county ST case rank severity R_e cases cases/100k daily cases
Josephine OR 22 1 2.1 160 190 2
Lane OR 8 2 1.5 736 200 11
Hood River OR 17 3 1.7 237 1020 2
Polk OR 14 4 1.4 436 540 6
Umatilla OR 4 5 1.1 2721 3540 18
Marion OR 2 6 0.9 3898 1160 36
Washington OR 3 7 0.9 3844 660 29
Multnomah OR 1 8 0.8 6087 760 37
Clackamas OR 5 9 0.9 1988 490 16
Malheur OR 6 10 0.8 1204 3960 14
Jackson OR 7 11 0.7 835 390 11
Deschutes OR 9 20 0.5 695 380 2
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Butte CA 30 1 1.6 2202 970 84
Los Angeles CA 1 2 0.9 243920 2420 1080
Tulare CA 13 3 1.3 14397 3130 139
Santa Cruz CA 31 4 1.5 1827 670 30
Colusa CA 40 5 1.8 462 2150 6
Santa Clara CA 11 6 1.1 17738 920 211
Riverside CA 2 7 1.0 53340 2240 334
San Diego CA 5 8 1.0 39196 1190 274
Sacramento CA 9 9 1.0 18477 1220 277
Alameda CA 8 11 1.0 18665 1140 198
Fresno CA 7 14 0.9 25482 2610 234
Orange CA 3 18 0.8 49141 1550 256
San Bernardino CA 4 19 0.8 48145 2250 266
Kern CA 6 31 0.8 29597 3350 124

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 1.0 134311 3160 256
Pinal AZ 4 2 1.0 9675 2310 48
Cochise AZ 11 3 1.3 1833 1450 5
Coconino AZ 8 4 1.0 3346 2390 11
Mohave AZ 6 5 0.9 3651 1770 14
Yavapai AZ 10 6 1.0 2327 1040 9
Navajo AZ 5 7 0.9 5622 5170 10
Yuma AZ 3 8 0.8 12272 5900 17
Pima AZ 2 9 0.5 21317 2090 40
Santa Cruz AZ 9 10 0.9 2762 5930 3
Apache AZ 7 12 0.7 3349 4680 4
CO
county ST case rank severity R_e cases cases/100k daily cases
San Miguel CO 32 1 2.2 94 1180 0
Lake CO 35 2 2.2 81 1070 0
Adams CO 3 3 0.9 7686 1550 46
Jefferson CO 5 4 1.0 4859 850 27
Boulder CO 7 5 1.1 2340 730 13
El Paso CO 4 6 0.9 6137 890 31
Denver CO 1 7 0.8 11345 1640 39
Weld CO 6 9 1.0 4098 1390 16
Arapahoe CO 2 10 0.8 8312 1310 34
Larimer CO 9 11 0.9 1990 590 17
Douglas CO 8 18 0.7 2132 650 13
UT
county ST case rank severity R_e cases cases/100k daily cases
Millard UT 14 1 2.9 148 1160 2
Uintah UT 16 2 2.1 102 280 2
Salt Lake UT 1 3 1.0 24346 2170 150
Weber UT 4 4 1.2 3352 1350 30
Davis UT 3 5 1.1 3987 1170 42
Utah UT 2 6 0.9 11184 1890 97
San Juan UT 9 7 1.7 669 4380 1
Washington UT 5 8 1.2 2840 1770 16
Cache UT 6 10 1.1 2137 1750 11
Tooele UT 8 13 1.0 703 1080 6
Summit UT 7 15 0.7 854 2110 4
NM
county ST case rank severity R_e cases cases/100k daily cases
Doña Ana NM 4 1 1.3 2894 1340 22
Otero NM 7 2 1.8 1125 1710 2
Grant NM 21 3 1.7 84 300 1
McKinley NM 2 4 1.3 4222 5800 9
Chaves NM 9 5 1.1 753 1150 14
Lincoln NM 17 6 1.5 171 880 1
Bernalillo NM 1 7 0.8 5862 860 23
Lea NM 6 9 0.8 1154 1650 10
San Juan NM 3 11 0.6 3200 2510 4
Sandoval NM 5 12 0.5 1257 890 4
Santa Fe NM 8 15 0.5 829 560 3

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Bergen NJ 1 1 1.4 21935 2360 44
Burlington NJ 12 2 1.3 6486 1450 22
Hudson NJ 3 3 1.3 20348 3040 20
Monmouth NJ 8 4 1.2 10873 1740 23
Passaic NJ 5 5 1.1 18553 3680 29
Essex NJ 2 6 1.1 20620 2600 30
Ocean NJ 7 7 1.1 11314 1910 34
Union NJ 6 9 1.1 17303 3130 18
Camden NJ 9 13 0.9 9254 1820 22
Middlesex NJ 4 16 0.8 18688 2260 18
PA
county ST case rank severity R_e cases cases/100k daily cases
Centre PA 32 1 2.1 523 320 20
Clinton PA 51 2 2.3 141 360 2
Philadelphia PA 1 3 1.3 34081 2160 142
York PA 12 4 1.4 3481 780 50
Lancaster PA 6 5 1.3 6848 1270 52
Chester PA 8 6 1.4 5744 1110 36
Delaware PA 3 7 1.3 10515 1870 58
Montgomery PA 2 12 1.0 11129 1360 45
Allegheny PA 4 13 1.0 10449 850 57
Bucks PA 5 16 1.1 7858 1250 29
Berks PA 7 21 0.9 6126 1470 30
Lehigh PA 9 22 1.1 5224 1440 11
MD
county ST case rank severity R_e cases cases/100k daily cases
Worcester MD 14 1 1.5 840 1630 12
Montgomery MD 2 2 1.1 20219 1940 80
Cecil MD 15 3 1.6 802 780 6
Caroline MD 18 4 1.6 520 1580 6
Baltimore MD 3 5 1.0 15705 1900 103
Anne Arundel MD 5 6 1.1 8473 1490 55
Prince George’s MD 1 7 1.0 26806 2960 96
Harford MD 8 9 1.0 2526 1010 23
Howard MD 6 11 1.0 4438 1410 22
Baltimore city MD 4 12 0.8 14601 2380 50
Frederick MD 7 15 0.8 3526 1420 15
Charles MD 9 17 0.8 2417 1530 12
VA
county ST case rank severity R_e cases cases/100k daily cases
Montgomery VA 28 1 2.1 578 590 39
Grayson VA 60 2 2.3 215 1360 9
Prince George VA 29 3 1.8 573 1510 14
Surry VA 86 4 2.0 75 1140 3
Richmond VA 44 5 2.1 332 3740 1
Mecklenburg VA 32 6 1.6 535 1730 8
Fairfax VA 1 7 1.1 18614 1630 105
Chesterfield VA 5 8 1.2 5137 1510 37
Loudoun VA 4 15 1.1 6050 1570 36
Prince William VA 2 16 0.9 10954 2400 60
Newport News city VA 9 17 1.1 2367 1310 26
Henrico VA 6 20 0.9 4677 1440 32
Virginia Beach city VA 3 26 0.9 6086 1350 33
Norfolk city VA 7 32 0.8 4432 1800 22
Arlington VA 8 46 0.7 3578 1540 14
WV
county ST case rank severity R_e cases cases/100k daily cases
Fayette WV 7 1 2.3 370 840 36
Monongalia WV 2 2 2.1 1194 1130 29
Lincoln WV 26 3 2.5 121 570 2
Brooke WV 30 4 2.2 94 410 2
Kanawha WV 1 5 1.5 1515 820 42
Randolph WV 16 6 1.9 227 780 1
Cabell WV 4 7 1.5 555 580 9
Raleigh WV 6 8 1.4 373 490 6
Mercer WV 9 9 1.4 318 530 6
Logan WV 5 15 1.0 500 1480 7
Jefferson WV 8 17 0.9 365 650 4
Berkeley WV 3 18 0.9 809 710 4
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1.2 8296 1490 54
Sussex DE 2 2 1.4 6364 2900 16
Kent DE 3 3 1.2 2645 1510 11

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Jefferson AL 1 1 1.1 16833 2550 188
Bullock AL 47 2 1.8 561 5420 4
Shelby AL 7 3 1.2 4735 2240 84
Madison AL 4 4 1.2 6609 1850 63
Lee AL 6 5 1.0 4883 3070 137
Calhoun AL 12 6 1.1 2643 2300 37
Montgomery AL 3 7 1.0 8089 3560 47
Baldwin AL 8 10 0.9 4610 2220 40
Mobile AL 2 11 0.8 12369 2980 57
Tuscaloosa AL 5 24 0.6 6014 2920 60
Marshall AL 9 48 0.7 3644 3830 10
MS
county ST case rank severity R_e cases cases/100k daily cases
Perry MS 69 1 2.5 310 2580 7
Lafayette MS 13 2 1.2 1522 2850 40
Yalobusha MS 65 3 1.5 378 3040 5
Oktibbeha MS 14 4 1.2 1490 3010 23
Tallahatchie MS 46 5 1.4 644 4480 6
Clarke MS 59 6 1.3 478 3000 8
Grenada MS 31 7 1.3 963 4530 8
DeSoto MS 2 10 0.9 4713 2680 42
Lee MS 7 13 1.0 2236 2630 26
Harrison MS 3 15 0.9 3373 1660 31
Jackson MS 4 17 0.9 3075 2170 30
Madison MS 5 22 0.9 3018 2920 23
Forrest MS 9 29 0.8 2159 2860 12
Hinds MS 1 30 0.6 6574 2720 23
Jones MS 8 31 0.8 2179 3180 9
Rankin MS 6 43 0.6 2881 1900 15
LA
county ST case rank severity R_e cases cases/100k daily cases
Plaquemines LA 49 1 2.9 571 2440 22
Orleans LA 3 2 1.7 11663 2990 75
Lincoln LA 38 3 1.7 924 1950 8
St. Tammany LA 7 4 1.3 6137 2430 43
Evangeline LA 31 5 1.5 1140 3390 18
East Baton Rouge LA 2 6 1.2 13904 3130 70
Tangipahoa LA 9 7 1.4 4092 3140 24
Lafayette LA 4 9 1.3 8320 3470 25
Jefferson LA 1 10 1.2 16574 3810 55
Calcasieu LA 6 14 1.2 7464 3730 24
Caddo LA 5 16 1.0 7487 3010 34
Ouachita LA 8 20 1.0 5526 3540 20

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Baker FL 50 1 2.1 1192 4290 15
Broward FL 2 2 1.4 72344 3790 444
Miami-Dade FL 1 3 1.2 159352 5870 901
Orange FL 5 4 1.3 36492 2760 236
Leon FL 22 5 1.4 6566 2280 101
Hillsborough FL 4 6 1.2 37655 2730 228
Seminole FL 16 7 1.4 8194 1800 61
Palm Beach FL 3 9 1.1 42519 2940 219
Polk FL 9 11 1.2 17276 2580 126
Duval FL 6 14 1.1 26795 2900 138
Lee FL 8 23 1.0 18887 2630 87
Pinellas FL 7 27 0.9 20082 2100 68
GA
county ST case rank severity R_e cases cases/100k daily cases
Lanier GA 128 1 3.1 267 2580 6
Clarke GA 18 2 1.6 3133 2510 95
Bulloch GA 29 3 1.5 2151 2880 90
Fannin GA 96 4 1.9 471 1890 11
Chattahoochee GA 44 5 1.4 1250 11610 43
Johnson GA 111 6 1.7 348 3580 10
Jenkins GA 114 7 1.7 330 3740 7
Hall GA 5 10 1.0 7881 4020 83
Gwinnett GA 2 13 0.8 24596 2730 132
Cobb GA 3 18 0.8 17215 2310 108
Chatham GA 6 19 1.0 7167 2500 48
DeKalb GA 4 20 0.9 16716 2250 72
Fulton GA 1 21 0.7 25167 2460 116
Muscogee GA 9 22 1.0 5517 2810 25
Richmond GA 8 31 0.8 6107 3030 40
Clayton GA 7 57 0.6 6566 2360 35

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Coryell TX 52 1 3.0 1450 1920 170
Brewster TX 151 2 3.3 198 2150 3
Duval TX 119 3 2.7 325 2860 24
Lubbock TX 18 4 1.9 7930 2630 180
Webb TX 11 5 1.7 11294 4150 179
Smith TX 26 6 1.8 3970 1760 96
Dallas TX 2 7 1.3 75631 2920 561
Harris TX 1 10 1.0 108382 2350 951
Bexar TX 3 22 1.0 46869 2430 167
Tarrant TX 4 23 0.9 42075 2080 207
El Paso TX 8 37 0.9 20647 2460 104
Nueces TX 9 52 0.7 19044 5280 76
Cameron TX 7 57 0.6 21327 5060 113
Travis TX 6 58 0.7 26631 2210 64
Hidalgo TX 5 66 0.4 27831 3280 126
OK
county ST case rank severity R_e cases cases/100k daily cases
Atoka OK 46 1 2.8 134 970 10
Muskogee OK 5 2 2.1 1456 2110 142
Texas OK 10 3 1.5 1174 5560 11
Kay OK 34 4 1.6 327 730 6
Payne OK 8 5 1.2 1245 1530 38
Tulsa OK 2 6 1.0 13524 2100 122
Cleveland OK 3 7 1.1 4034 1460 56
Oklahoma OK 1 9 1.0 13684 1750 114
Wagoner OK 9 17 1.1 1177 1510 14
Canadian OK 4 33 0.8 1560 1140 12
Rogers OK 6 34 0.8 1336 1470 10
Comanche OK 7 42 0.5 1275 1040 13

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Ottawa MI 9 1 2.0 2285 800 50
Lenawee MI 14 2 1.9 1352 1370 21
Houghton MI 57 3 2.1 84 230 5
Delta MI 43 4 1.5 166 460 5
Oakland MI 2 5 0.8 18254 1460 88
Washtenaw MI 6 6 1.1 3463 950 20
Gladwin MI 60 7 1.7 79 310 1
Jackson MI 7 9 1.2 2578 1620 10
Wayne MI 1 10 0.7 31510 1790 108
Macomb MI 3 13 0.7 13194 1520 70
Kent MI 4 15 0.8 8578 1330 42
Saginaw MI 8 26 0.7 2563 1330 16
Genesee MI 5 32 0.7 3993 980 11
WI
county ST case rank severity R_e cases cases/100k daily cases
Juneau WI 40 1 2.2 238 900 14
Kewaunee WI 42 2 1.8 192 940 6
Portage WI 23 3 1.4 649 920 19
Outagamie WI 7 4 1.2 1979 1070 49
Adams WI 51 5 1.7 122 610 4
Dodge WI 13 6 1.2 1204 1370 22
Door WI 48 7 1.7 134 490 2
Dane WI 3 9 1.1 5563 1050 49
Kenosha WI 6 12 1.2 2998 1780 15
Milwaukee WI 1 19 0.8 24279 2540 84
Rock WI 8 20 1.1 1882 1160 19
Brown WI 4 22 0.8 5510 2120 50
Waukesha WI 2 28 0.8 5651 1420 34
Racine WI 5 33 0.8 4484 2290 18
Walworth WI 9 40 0.8 1726 1680 10

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Winona MN 20 1 1.8 510 1000 32
Chippewa MN 48 2 1.9 140 1170 2
Clay MN 17 3 1.6 908 1450 12
Blue Earth MN 10 4 1.3 1307 1970 30
Hennepin MN 1 5 0.9 23409 1890 161
Lyon MN 19 6 1.4 547 2120 13
Dakota MN 3 7 1.0 6085 1460 81
Scott MN 8 9 1.2 2060 1440 25
Anoka MN 4 12 1.0 4828 1390 49
Ramsey MN 2 14 0.9 9323 1720 67
Washington MN 6 16 0.9 3069 1210 40
Olmsted MN 7 18 1.1 2062 1350 15
Stearns MN 5 22 0.9 3307 2110 20
Nobles MN 9 44 0.8 1872 8570 4
SD
county ST case rank severity R_e cases cases/100k daily cases
Todd SD 23 1 2.4 82 810 1
Brookings SD 7 2 1.6 369 1080 27
Codington SD 8 3 1.5 356 1270 20
Minnehaha SD 1 4 1.2 5395 2890 60
Clay SD 6 5 1.3 402 2890 28
Yankton SD 11 6 1.5 247 1090 10
Lincoln SD 3 7 1.2 938 1710 18
Pennington SD 2 8 0.9 1500 1370 39
Brown SD 4 10 1.0 706 1820 16
Meade SD 9 16 0.7 324 1180 11
Beadle SD 5 19 0.7 642 3490 2
ND
county ST case rank severity R_e cases cases/100k daily cases
Williams ND 7 1 1.4 468 1370 17
Stutsman ND 9 2 1.4 228 1080 11
Cass ND 1 3 1.1 3600 2070 35
Stark ND 4 4 1.1 754 2440 25
Burleigh ND 2 5 0.9 2093 2230 38
Morton ND 5 6 1.1 674 2210 15
Walsh ND 12 7 1.5 145 1340 1
Benson ND 8 8 1.2 238 3460 4
Grand Forks ND 3 11 0.7 1557 2210 35
Ward ND 6 12 0.7 522 760 9

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Middlesex CT 6 1 1.7 1468 900 6
New Haven CT 2 2 1.2 13732 1600 29
Windham CT 8 3 1.5 810 700 5
New London CT 5 4 1.3 1593 590 10
Fairfield CT 1 5 1.0 18971 2010 47
Hartford CT 3 6 0.9 13567 1520 42
Litchfield CT 4 7 1.2 1719 940 8
Tolland CT 7 8 1.2 1154 760 7
MA
county ST case rank severity R_e cases cases/100k daily cases
Bristol MA 6 1 1.0 9836 1760 24
Middlesex MA 1 2 0.8 27763 1740 52
Suffolk MA 2 3 0.7 23587 2980 57
Barnstable MA 9 4 1.2 1866 870 5
Worcester MA 4 5 0.8 14226 1730 22
Hampden MA 8 6 0.9 7958 1700 14
Essex MA 3 7 0.6 18932 2420 31
Norfolk MA 5 8 0.8 11060 1580 16
Plymouth MA 7 9 0.8 9656 1890 15
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.3 16930 2670 92
Newport RI 4 2 1.6 448 540 5
Washington RI 3 3 1.3 711 560 6
Kent RI 2 4 1.0 1690 1030 9
Bristol RI 5 5 0.8 355 730 2

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Otsego NY 42 1 3.1 185 310 16
Cortland NY 50 2 2.6 102 210 1
Delaware NY 49 3 2.0 118 260 2
New York City NY 1 4 1.0 239516 2840 284
Tompkins NY 35 5 1.8 277 270 5
Nassau NY 3 6 1.3 44830 3300 72
Suffolk NY 2 7 1.2 44933 3020 56
Erie NY 7 8 1.2 10008 1090 66
Rockland NY 5 10 1.3 14298 4420 22
Westchester NY 4 13 1.1 37059 3830 39
Orange NY 6 20 1.0 11474 3030 13
Monroe NY 8 29 0.7 5475 740 12
Dutchess NY 9 30 0.8 4880 1660 9

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Franklin VT 4 1 2.2 123 250 0
Chittenden VT 1 2 1.5 804 500 4
Rutland VT 2 3 1.5 124 210 3
Windsor VT 7 4 1.2 80 140 0
Bennington VT 5 5 1.0 104 290 1
Addison VT 6 6 0.5 81 220 0
Windham VT 3 7 0.2 123 290 0
ME
county ST case rank severity R_e cases cases/100k daily cases
York ME 2 1 0.9 854 420 10
Cumberland ME 1 2 1.0 2198 760 4
Kennebec ME 5 3 1.0 192 160 1
Androscoggin ME 3 4 0.8 617 570 2
Penobscot ME 4 5 0.8 233 150 2
NH
county ST case rank severity R_e cases cases/100k daily cases
Hillsborough NH 1 1 1.2 4054 990 8
Strafford NH 4 2 1.4 390 300 2
Rockingham NH 2 3 0.8 1818 600 4
Merrimack NH 3 4 0.7 504 340 1
Cheshire NH 5 5 0.5 131 170 1
Grafton NH 7 6 0.6 114 130 0
Carroll NH 8 7 0.5 105 220 0
Belknap NH 6 8 0.6 125 210 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.4 11517 2820 204
Abbeville SC 43 2 1.7 438 1780 8
Lexington SC 5 3 1.2 5860 2050 48
Florence SC 9 4 1.2 4353 3140 42
York SC 10 5 1.1 4337 1680 39
Spartanburg SC 6 6 1.1 5114 1690 45
Charleston SC 1 7 1.0 14194 3600 82
Berkeley SC 7 12 1.0 4876 2330 26
Beaufort SC 8 15 0.9 4815 2640 25
Greenville SC 2 17 0.7 12228 2450 47
Horry SC 4 19 0.8 9432 2940 28
NC
county ST case rank severity R_e cases cases/100k daily cases
New Hanover NC 14 1 1.7 3149 1400 50
Pamlico NC 86 2 1.9 178 1400 15
Currituck NC 91 3 2.0 112 430 4
Harnett NC 28 4 1.5 1687 1290 26
Mecklenburg NC 1 5 1.1 25920 2460 172
Guilford NC 3 6 1.1 7022 1340 73
Alamance NC 12 7 1.2 3274 2040 48
Union NC 8 8 1.1 4071 1800 43
Johnston NC 9 10 1.2 3955 2070 37
Wake NC 2 13 0.9 15468 1480 149
Cumberland NC 6 17 1.0 4159 1250 46
Forsyth NC 5 18 1.0 6265 1690 45
Gaston NC 7 33 0.9 4156 1920 31
Durham NC 4 39 0.9 7012 2290 31

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Silver Bow MT 11 1 1.9 115 330 1
Yellowstone MT 1 2 1.0 2158 1370 40
Rosebud MT 7 3 1.1 298 3220 17
Gallatin MT 2 4 1.2 1105 1060 8
Big Horn MT 3 5 1.0 682 5100 10
Flathead MT 4 6 0.8 626 640 13
Phillips MT 12 7 1.2 113 2740 1
Cascade MT 6 10 0.7 299 370 6
Lewis and Clark MT 9 11 1.0 198 300 1
Missoula MT 5 12 0.7 440 380 2
Lake MT 8 14 0.7 204 690 1
WY
county ST case rank severity R_e cases cases/100k daily cases
Park WY 9 1 2.1 169 580 2
Albany WY 11 2 1.7 145 380 4
Uinta WY 5 3 1.5 303 1470 3
Lincoln WY 12 4 1.5 114 600 1
Campbell WY 7 5 1.0 200 420 4
Sweetwater WY 4 6 1.1 304 690 2
Natrona WY 6 7 0.9 297 370 3
Laramie WY 2 8 0.9 576 590 3
Carbon WY 8 9 1.0 192 1240 1
Teton WY 3 12 0.6 433 1880 2
Fremont WY 1 13 0.5 610 1520 2
ID
county ST case rank severity R_e cases cases/100k daily cases
Bingham ID 12 1 2.3 548 1200 28
Bannock ID 6 2 2.1 759 890 30
Washington ID 17 3 1.7 287 2860 5
Madison ID 18 4 1.5 260 670 6
Ada ID 1 5 1.0 11361 2550 77
Power ID 24 6 1.4 151 1960 6
Bonneville ID 4 7 1.0 1882 1670 29
Canyon ID 2 8 0.9 7314 3450 45
Kootenai ID 3 10 0.9 2215 1440 13
Payette ID 7 12 0.9 702 3050 13
Twin Falls ID 5 15 0.9 1701 2030 9
Jerome ID 8 18 0.9 612 2610 4
Blaine ID 9 26 0.6 607 2760 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Ashland OH 73 1 2.5 172 320 2
Montgomery OH 5 2 1.6 6202 1170 174
Guernsey OH 77 3 2.3 135 340 2
Butler OH 7 4 1.5 4276 1130 110
Muskingum OH 53 5 2.0 305 350 3
Franklin OH 1 6 1.1 22162 1740 178
Portage OH 27 7 1.6 876 540 9
Hamilton OH 3 8 1.1 11363 1400 89
Cuyahoga OH 2 10 1.0 15918 1270 99
Lucas OH 4 17 1.0 6523 1510 47
Summit OH 6 21 1.0 4546 840 43
Marion OH 8 57 0.9 3008 4600 2
Mahoning OH 9 60 0.6 2870 1240 7
IL
county ST case rank severity R_e cases cases/100k daily cases
McLean IL 14 1 1.6 2048 1180 151
Greene IL 70 2 2.2 125 950 6
McDonough IL 55 3 1.9 227 740 11
Ogle IL 31 4 1.9 513 1000 9
Morgan IL 30 5 1.8 533 1550 12
Iroquois IL 43 6 1.9 309 1100 4
Wayne IL 73 7 1.9 117 710 5
Cook IL 1 9 0.9 127678 2440 600
Winnebago IL 7 13 1.2 4316 1510 33
Madison IL 8 15 1.0 4174 1570 62
Kane IL 5 18 1.0 11381 2140 59
DuPage IL 2 19 0.9 14778 1590 93
St. Clair IL 6 20 1.0 5991 2270 62
Will IL 4 24 0.9 11760 1710 90
Lake IL 3 32 0.8 14720 2090 64
McHenry IL 9 44 0.8 3970 1290 27
IN
county ST case rank severity R_e cases cases/100k daily cases
Delaware IN 16 1 1.5 1283 1110 54
Vigo IN 14 2 1.4 1380 1280 47
Monroe IN 17 3 1.4 1232 850 44
Montgomery IN 42 4 1.6 444 1160 8
Marion IN 1 5 1.0 18879 2000 127
Madison IN 15 6 1.2 1353 1040 18
Owen IN 76 7 1.6 150 720 3
Hamilton IN 6 9 0.9 4038 1280 50
Hendricks IN 8 13 1.0 2399 1490 23
Lake IN 2 16 0.8 9285 1910 49
Allen IN 5 18 0.8 5086 1370 38
Vanderburgh IN 7 20 0.9 2635 1450 25
Elkhart IN 3 36 0.7 5730 2810 21
St. Joseph IN 4 39 0.4 5253 1950 41
Johnson IN 9 43 0.7 2090 1380 10

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Wayne TN 21 1 4.5 1621 9740 510
Van Buren TN 94 2 1.7 86 1510 5
Knox TN 5 3 1.1 6985 1530 101
Washington TN 16 4 1.3 1803 1420 29
Franklin TN 58 5 1.4 530 1280 14
Sequatchie TN 86 6 1.8 147 1000 2
Hamilton TN 3 7 1.0 8203 2290 78
Wilson TN 8 8 1.2 2896 2180 29
Williamson TN 6 9 1.1 4457 2040 38
Shelby TN 1 12 0.8 27852 2970 129
Davidson TN 2 16 0.8 26149 3820 81
Rutherford TN 4 20 0.9 7922 2580 42
Sumner TN 7 27 0.9 4142 2310 25
Bradley TN 9 37 0.9 2520 2410 18
KY
county ST case rank severity R_e cases cases/100k daily cases
Nelson KY 29 1 2.5 385 850 17
Carter KY 73 2 2.7 124 450 3
Mercer KY 74 3 2.3 123 570 4
Larue KY 80 4 2.3 105 740 3
Fayette KY 2 5 1.2 5910 1850 111
Jefferson KY 1 6 1.1 12816 1670 191
Greenup KY 44 7 1.7 219 610 8
Madison KY 6 11 1.2 1112 1240 40
Warren KY 3 19 1.0 3414 2700 40
Boone KY 5 27 1.1 1319 1020 11
Kenton KY 4 31 1.1 1745 1060 12
Hardin KY 8 45 0.9 948 880 10
Shelby KY 9 48 1.0 914 1950 5
Daviess KY 7 51 0.9 1005 1010 9

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Gentry MO 75 1 2.3 98 1470 2
Dunklin MO 26 2 1.9 526 1730 14
Polk MO 36 3 1.9 348 1100 10
Boone MO 7 4 1.4 2856 1620 117
Adair MO 47 5 1.8 247 980 7
St. Louis MO 1 6 1.1 19873 1990 214
Webster MO 50 7 1.6 242 640 10
Jackson MO 4 8 1.2 5692 820 81
Greene MO 5 9 1.1 3587 1240 116
St. Louis city MO 2 14 1.2 6262 2010 42
St. Charles MO 3 15 1.0 5908 1510 70
Jefferson MO 6 18 1.0 2902 1300 49
Jasper MO 8 28 1.0 1670 1400 21
Clay MO 9 37 0.9 1414 590 15
AR
county ST case rank severity R_e cases cases/100k daily cases
Washington AR 2 1 1.2 6988 3060 45
Franklin AR 55 2 1.7 172 970 3
Benton AR 3 3 1.2 5448 2100 41
Craighead AR 6 4 1.1 1925 1820 29
Boone AR 33 5 1.1 382 1020 13
Yell AR 16 6 1.2 1170 5420 7
Pulaski AR 1 7 0.8 7183 1830 51
Pope AR 7 8 0.9 1797 2820 19
Sebastian AR 4 17 0.7 2917 2290 18
Faulkner AR 9 20 0.7 1722 1410 16
Hot Spring AR 8 26 0.9 1727 5150 4
Jefferson AR 5 29 0.6 2135 3030 15

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1193.2 seconds to compute.
2020-09-03 08:08:27

version history

Today is 2020-09-03.
106 days ago: Multiple states.
98 days ago: \(R_e\) computation.
95 days ago: created color coding for \(R_e\) plots.
90 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
90 days ago: “persistence” time evolution.
83 days ago: “In control” mapping.
83 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
75 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
70 days ago: Added Per Capita US Map.
68 days ago: Deprecated national map.
64 days ago: added state “Hot 10” analysis.
59 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
57 days ago: added per capita disease and mortaility to state-level analysis.
45 days ago: changed to county boundaries on national map for per capita disease.
40 days ago: corrected factor of two error in death trend data.
36 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
31 days ago: added county level “baseline control” and \(R_e\) maps.
27 days ago: fixed normalization error on total disease stats plot.
20 days ago: Corrected some text matching in generating county level plots of \(R_e\).
14 days ago: adapter knot spacing for spline.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.